Detect and Calculate the Speed of Moving Objects Using R-CNN Technique

Artificial intelligence (AI) algorithms depend on different algorithms to track moving objects. The surrounding environment, like lightness, affects detection accuracy. A new algorithm is designed to detect moving objects in real time automatically and calculate the speed of tested objects based on the deep learning algorithm RCNN using MATLAB software. The suggested system consists of a phone camera, four balls in different colors (red, green, blue, and black), and various environmental lights consisting of eight lights. Two luxmeters were used to check the light intensity around the ball and the camera phone. Detecting moving objects has gained a lot of desirability because of its applications like video surveillance, person movement tracking, traffic investigation, and security systems like security systems or surveillance. are four parameters used to evaluate the performance of the algorithms and the system setup: accuracy, average time, detection percentage, and speed. Results show that the quality of detecting and tracking the ball is almost 100%.

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Publication Details

Journal
SHILAP Revista de lepidopterología
Published
2025-04-01
DOI
https://doi.org/10.30526/38.2.4020
Primary Topic
Advanced Neural Network Applications
Type
article
Field-Weighted Citation Impact
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article

Detect and Calculate the Speed of Moving Objects Using R-CNN Technique

SHILAP Revista de lepidopterología
Advanced Neural Network Applications
article

Detect and Calculate the Speed of Moving Objects Using R-CNN Technique

article en

Abstract

Artificial intelligence (AI) algorithms depend on different algorithms to track moving objects. The surrounding environment, like lightness, affects detection accuracy. A new algorithm is designed to detect moving objects in real time automatically and calculate the speed of tested objects based on the deep learning algorithm RCNN using MATLAB software. The suggested system consists of a phone camera, four balls in different colors (red, green, blue, and black), and various environmental lights consisting of eight lights. Two luxmeters were used to check the light intensity around the ball and the camera phone. Detecting moving objects has gained a lot of desirability because of its applications like video surveillance, person movement tracking, traffic investigation, and security systems like security systems or surveillance. are four parameters used to evaluate the performance of the algorithms and the system setup: accuracy, average time, detection percentage, and speed. Results show that the quality of detecting and tracking the ball is almost 100%.

SHILAP Revista de lepidopterología
University of Baghdad (IQ)
Sustainable cities and communities
Openalex Percentile: Top 53%
Advanced Neural Network Applications
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